1. How are price benchmarks within ETF classifications calculated in P123? I know how it is done with stocks (i.e., a modified capitalization weighting with some filters). But what does "Close(0,#Style)" in an ETF context mean?

2. Is there a way to create price benchmarks of ETFs based on multiple criteria? Let's say I wanted to create price index of ETF within the same Style and Asset Class. Could I do this natively in P123 going back in time?

3. Has anyone thought of or found a way to create performance benchmarks based on quantitatively similar securities within P123, going back in time, and by using native P123 syntax. This could be similar to Yuval's correlation clustering algorithm, but doesn't necessarily have to involve Pearson correlation.

The reason I ask these questions is that I think they are germaine to parallel discussions on global equity momentum. I strongly suspect that any remaining inefficiencies in trend following going forward depend largely on selecting/constructing a good benchmark against which to measure performance. The challenge, however, is that a good ETF benchmark is not easily defined by looking at the cross-sectional performance against any single ETF classifier.

I mean, it makes no sense to me to measure a standard ETF against an inverse leveraged ETF in the same asset class. However, the performance of peers in the same asset class should be considered.

Maybe there's a simple way to cut through this classification problem?

*** QUICK UPDATE ***

I think it might be elegant to implement more granular peer groups through a “double sort” option. For example, this could be implemeted if composite nodes in of ranking systems were able to be ranked within a scope. The logic might look as such (the ETFComposite node and scope method in the node are not currently interpreted):